Machine Learning with Python and Scikit-Learn – Full Course

Embark on a comprehensive 18-hour journey into Machine Learning with Python and Scikit-Learn, designed for beginners with basic Python and statistics knowledge. Explore fundamental concepts like linear and logistic regression before advancing to tree-based models such as decision trees, random forests, and gradient-boosting machines. Learn best practices for managing machine learning projects, build a state-of-the-art model for real-world data, and delve into unsupervised learning and recommendations. Gain hands-on experience by following along with provided code notebooks, and conclude by deploying a machine learning model to the cloud using Flask. By the end, confidently build, train, and deploy real-world machine learning models, with encouragement to apply newly acquired skills to datasets and competitions on platforms like Kaggle.

⌨️ Introduction
⌨️ Lesson 1 - Linear Regression and Gradient Descent
⌨️ Lesson 2 - Logistic Regression for Classification
⌨️ Lesson 3 - Decision Trees and Random Forests
⌨️ Lesson 4 - How to Approach Machine Learning Projects
⌨️ Lesson 5 - Gradient Boosting Machines with XGBoost
⌨️ Lesson 6 - Unsupervised Learning using Scikit-Learn
⌨️ Lesson 7 - Machine Learning Project from Scratch
⌨️ Lesson 8 - Deploying a Machine Learning Project with Flask